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- item-1 hasContent steven-aanen.
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- 199 creator alessio-fabiani.
- 199 creator cosmin-stroe.
- 199 creator federico-caimi.
- 199 creator isabel-f-cruz.
- 199 creator matteo-palmonari.
- 199 type InProceedings.
- 199 label "Automatic Configuration Selection Using Ontology Matching Task Profiling".
- 199 sameAs 199.
- 199 abstract "An ontology matching system can usually be run with different configurations that optimize the system's effectiveness, namely precision, recall, or F-measure, depending on the specific ontologies to be aligned. Changing the configuration has potentially high impact on the obtained results. We apply matching task profiling metrics to automatically optimize the system's configuration depending on the characteristics of the ontologies to be aligned. Using machine learning techniques, we can automatically determine the optimal configuration in most cases. Even using a small training set, our system predicts the best configuration in 94% of the cases. Our approach is evaluated using our extensible and configurable ontology matching system AgreementMaker.".
- 199 hasAuthorList authorList.
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- 199 keyword "automatic configuration selection".
- 199 keyword "machine learning".
- 199 keyword "matching task profiling".
- 199 keyword "ontology alignment".
- 199 keyword "ontology matching".
- 199 keyword "ontology profiling".
- 199 title "Automatic Configuration Selection Using Ontology Matching Task Profiling".
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- 20 creator christine-solnon.
- 20 creator pierre-antoine-champin.
- 20 creator vianney-le-clement-de-saint-marcq.
- 20 creator yves-deville.
- 20 type InProceedings.
- 20 label "Castor: a Constraint-based SPARQL Engine with Active Filter Processing".
- 20 sameAs 20.
- 20 abstract "Efficient evaluation of complex SPARQL queries is still an open research problem. State-of-the-art engines are based on relational database technologies. We approach the problem from the perspective of Constraint Programming (CP), a technology designed for solving NP-hard problems. Such technology allows us to exploit SPARQL filters early-on during the search instead of as a post-processing step. We propose Castor, a new SPARQL engine based on CP. Castor performs very competitively compared to state-of-the-art engines.".
- 20 hasAuthorList authorList.
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- 20 keyword "Constraint Programming".
- 20 keyword "Query engine implementation".
- 20 keyword "SPARQL".
- 20 title "Castor: a Constraint-based SPARQL Engine with Active Filter Processing".
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- 213 creator christian-chiarcos.
- 213 type InProceedings.
- 213 label "POWLA: Modeling linguistic corpora in OWL/DL".
- 213 sameAs 213.
- 213 abstract "This paper describes POWLA, a formalism to formalize linguistic corpora in OWL/DL. POWLA is based on data models currently developed by the NLP community to overcome the heterogeneity of linguistic annotation (Ide and Pustejovsky 2010), in particular, PAULA, an XML standoff format developed out of early sketches of the Linguistic Annotation Framework (LAF, Ide and Romary 2004) which is currently developed within ISO TC37/SC4. These data models are defined as specializations of directed acyclic (hyper)graphs, and it is claimed that every kind of linguistic annotation can be represented as a directed (hyper)graph (Bird and Liberman 2001). Linguistic corpora can thus be naturally linearized in RDF. Unlike earlier approaches to model generic data models for linguistic annotations by means of Semantic Web standards (e.g., Cassidy 2010), POWLA augments the RDF linearization of linguistic data with a data model formalized in an OWL/DL ontology that defines data types for primary data, annotations and linguistic metadata, as well as consistency constraints on linguistic corpora. Unlike other approaches to model linguistic corpora in OWL/DL (e.g., Burchardt et al. 2008), POWLA is not specific to a particular type of annotation, but it implements a generic data model. This genericity is illustrated here for the conversion of GrAF (the XML linearization of the Linguistic Annotation Format, Ide and Suderman 2007) to POWLA. That POWLA preserves the linguistic information conveyed in the original GrAF data as shown by an experient to emulate ANNIS-QL, a query language specifically designed for heterogeneous and richly annotated linguistic corpora (Chiarcos et al. 2008), by means of SPARQL macros on POWLA data. Finally, the paper identifies advantages and disadvantages of OWL/RDF linearizations of generic data models for linguistic corpora (and in particular, POWLA) as compared to traditional XML standoff formats (Ide and Suderman 2007, Chiarcos et al. 2008).".
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